Application of constriction coefficient-based particle swarm optimisation and gravitational search algorithm for solving practical engineering design problems

Rather, Sajad Ahmad; Bala, P. Shanthi

doi:10.1504/ijbic.2021.116617

Cited by 13 publications

(7 citation statements)

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“…Next, we will introduce some classical scheduling algorithms and two kinds of intelligent multiobjective optimization [54,55] task scheduling algorithms based on heuristic ideas.…”

Section: Scheduling Algorithmmentioning

confidence: 99%

Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

Zhang¹,

Geng²,

Cai³

2023

Computer Modeling in Engineering &Amp; Sciences

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Cloud computing technology is favored by users because of its strong computing power and convenient services. At the same time, scheduling performance has an extremely efficient impact on promoting carbon neutrality. Currently, scheduling research in the multi-cloud environment aims to address the challenges brought by business demands to cloud data centers during peak hours. Therefore, the scheduling problem has promising application prospects under the multi-cloud environment. This paper points out that the currently studied scheduling problems in the multi-cloud environment mainly include independent task scheduling and workflow task scheduling based on the dependencies between tasks. This paper reviews the concepts, types, objectives, advantages, challenges, and research status of task scheduling in the multi-cloud environment. Task scheduling strategies proposed in the existing related references are analyzed, discussed, and summarized, including research motivation, optimization algorithm, and related objectives. Finally, the research status of the two kinds of task scheduling is compared, and several future important research directions of multi-cloud task scheduling are proposed.

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“…Next, we will introduce some classical scheduling algorithms and two kinds of intelligent multiobjective optimization [54,55] task scheduling algorithms based on heuristic ideas.…”

Section: Scheduling Algorithmmentioning

confidence: 99%

Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

Zhang¹,

Geng²,

Cai³

2023

Computer Modeling in Engineering &Amp; Sciences

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“…In this article, the comprehensive fitness estimation method is used to screen the optimal solution obtained in the process of population evolution 52 . If hypothetical population

P=\left\{{p}_1,{p}_2,\dots, {p}_N\right\}

contain N individuals, the CFE method can be described as follows.…”

Section: Design Algorithmmentioning

confidence: 99%

A novel multi‐objective immune optimization algorithm for under sampling software defect prediction problem

Ye¹,

Li²,

Zhang

et al. 2022

Concurrency and Computation

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Summary Data imbalance and parameter selection have always been two important factors affecting the accuracy of software defect prediction. However, the existing methods have been poor in balancing these two factors. To address this challenge, a multi‐objective software defect prediction model is employed to describe the under sampled software defect prediction problem. And in this model, defect detection rate and defect false alarm rate are deemed as two objectives which should be optimized. Simultaneously, we design a novel multi‐objective immune optimization algorithm based on the comprehensive fitness of evaluation mechanism to effectively address the employed model. In the algorithm, the original mechanism based on neighborhood individual selection is replaced based on comprehensive fitness evaluation,which have better selection ability to attaining the predicted effect of software to improving in the process of selection solution of population evolution and further effectively help decision makers choose better scheme that meets requirements. In addition, in order to verify the effectiveness of the designed algorithm, the proposed algorithm is compared on eight different public data sets. Simulation results show that the proposed algorithm has better performance in handling with the multi‐objective under sampling software defect prediction problem.

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“…where z nadir and z * denote the nadir and ideal points, respectively, and X key is calculated with the following angle defined with Equation (7) 3 :…”

Section: Given One Solution Setmentioning

confidence: 99%

“…Multi‐objective optimization can be found in various engineering problems, 1‐6 which mathematically have two or three conflicting objectives. The aim of effectively tackling multi‐objective optimization problems (MOPs) 7‐9 is at the best trade‐off solutions, which are defined as Pareto fronts in the objective space.…”

Section: Introductionmentioning

confidence: 99%

Many‐objective evolutionary algorithm assisted by a novel angle‐based fitness strategy

Zhu¹

2022

Concurrency and Computation

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Measuring the convergence of solutions is greatly significant for many-objective optimization problems. Although various methods have been proposed regarding this issue, they still suffer from the performance consistency on different problems. For this issue, this article proposes a novel angle-based fitness strategy. To be specific, the convergence of solutions is defined with adaptive key solutions, which are determined by the angles between solutions. Based on the novel angle-based fitness strategy, this article designs a many-objective evolutionary algorithm assisted by a novel angle-based fitness strategy. Experiments are conduced to verify the performance of the proposed method in comparison with other state-of-the-art methods. The experimental analyzes illustrate the outstanding performance of the proposed method for many-objective optimization problems.

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Application of constriction coefficient-based particle swarm optimisation and gravitational search algorithm for solving practical engineering design problems

Cited by 13 publications

References 0 publications

Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

Survey on Task Scheduling Optimization Strategy under Multi-Cloud Environment

A novel multi‐objective immune optimization algorithm for under sampling software defect prediction problem

Many‐objective evolutionary algorithm assisted by a novel angle‐based fitness strategy

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